Galaxy Formation and Evolution via Phase-temporal Clustering with FuzzyCat $\circ$ AstroLink
William H. Oliver, Tobias Buck

TL;DR
This paper introduces a novel unsupervised clustering pipeline combining AstroLink and FuzzyCat to analyze galaxy formation, revealing detailed hierarchical structures in simulated galaxies without strong assumptions.
Contribution
The paper presents a new composition of clustering algorithms that effectively identifies hierarchical galaxy structures in data with minimal user input and assumptions.
Findings
Successfully decomposed simulated galaxies into multiple structural components.
Revealed diverse galaxy features such as dwarf galaxies, streams, and bulges.
Enhanced understanding of galaxy hierarchy and formation processes.
Abstract
We demonstrate how the composition of two unsupervised clustering algorithms, and , makes for a powerful tool when studying galaxy formation and evolution. is a general-purpose astrophysical clustering algorithm built for extracting meaningful hierarchical structure from point-cloud data defined over any feature space, while is a generalised soft-clustering algorithm that propagates the dynamical effects of underlying data processes into a fuzzy hierarchy of stable fuzzy clusters. Their composition, , can therefore identify a fuzzy hierarchy of astrophysically- and statistically-significant fuzzy clusters within any point-based data set whose representation is subject to changes caused by some underlying process. Furthermore, the pipeline achieves this without…
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Taxonomy
TopicsAstronomical Observations and Instrumentation
